qpcR (version 1.4-1)

ratioPar: Calculation of ratios in a batch format from external PCR parameters

Description

Starting from external PCR parameters such as threshold cycles/efficiency values commonly obtained from other programs, this function calculates ratios between samples, using normalization against one or more reference gene(s), if supplied. By default, multiple reference genes are averaged according to Vandesompele et al. (2002). The input can be single qPCR data or (more likely) data containing replicates. It is similar to ratiobatch and can handle multiple reference genes and genes-of-interest with multiple (replicated) samples as found in large-scale qPCR runs such as 96- or 384-Well plates. The results are automatically stored as a file or copied into the clipboard. A boxplot representation for all Monte-Carlo simulations, permutations and error propagations including 95% confidence intervals is also given.

Usage

ratioPar(group = NULL, effVec = NULL, cpVec = NULL, 
         type.eff = "individual", plot = TRUE, 
         combs = c("same", "across", "all"), 
         refmean = FALSE, verbose = TRUE, ...)

Arguments

group

a character vector defining the replicates (if any) of control/treatment samples and reference genes/genes-of-interest. See 'Details'.

effVec

a vector of efficiency values with the same length of group.

cpVec

a vector of threshold cycle values with the same length of group.

type.eff

type of efficiency averaging used. Same as in ratiocalc.

plot

logical. If TRUE, plots are displayed for the diagnostics and analysis.

combs

type of combinations between different samples (i.e. r1s1:g1s2). See 'Details'.

refmean

logical. If TRUE, multiple reference are averaged before calculating the ratios. See 'Details'.

verbose

logical. If TRUE, the steps of analysis are shown in the console window

...

other parameters to be passed to ratiocalc.

Value

A list with the following components:

resList

a list with the results from the combinations as list items.

resDat

a dataframe with the results in colums.

Both resList and resDat have as names the combinations used for the ratio calculation. If plot = TRUE, a boxplot matrix from the Monte-Carlo simulations, permutations and error propagations is given including 95% confidence intervals as coloured horizontal lines.

Details

As in ratiobatch, the replicates are to be defined as a character vector with the following abbreviations:

"g1s1": gene-of-interest #1 in treatment sample #1 "g1c1": gene-of-interest #1 in control sample #1 "r1s1": reference gene #1 in treatment sample #1 "r1c1": reference gene #1 in control sample #1

There is no distinction between the different technical replicates so that three different runs of gene-of-interest #1 in treatment sample #2 are defined as c("g1s2", "g1s2", "g1s2").

Example: 1 control sample with 2 genes-of-interest (2 technical replicates), 2 treatment samples with 2 genes-of-interest (2 technical replicates): "g1c1", "g1c1", "g2c1", "g2c1", "g1s1", "g1s1", "g1s2", "g1s2", "g2s1", "g2s1", "g2s2", "g2s2"

The ratios are calculated for all pairwise 'rc:gc' and 'rs:gs' combinations according to: For all control samples \(i = 1 \ldots I\) and treatment samples \(j = 1 \ldots J\), reference genes \(k = 1 \ldots K\) and genes-of-interest \(l = 1 \ldots L\), calculate

Without reference genes: $$\frac{E(g_lc_i)^{cp(g_lc_i)}}{E(g_ls_j)^{cp(g_ls_j)}}$$ With reference genes: $$\frac{E(g_lc_i)^{cp(g_lc_i)}}{E(g_ls_j)^{cp(g_ls_j)}}/\frac{E(r_kc_i)^{cp(r_kc_i)}}{E(r_ks_j)^{cp(r_ks_j)}}$$ For the mechanistic models makX/cm3 the following is calculated:

Without reference genes: $$\frac{D_0(g_ls_j)}{D_0(g_lc_i)}$$ With reference genes: $$\frac{D_0(g_ls_j)}{D_0(g_lc_i)}/\frac{D_0(r_ks_j)}{D_0(r_kc_i)}$$

Efficiencies can be taken from the individual samples or averaged from the replicates as described in the documentation to ratiocalc. Different settings in type.eff can yield very different results in ratio calculation. We observed a relatively stable setup which minimizes the overall variance using the type.eff = "mean.single".

There are three different combination setups possible when calculating the pairwise ratios: combs = "same": reference genes, genes-of-interest, control and treatment samples are the same, i.e. \(i = k, m = o, j = n, l = p\). combs = "across": control and treatment samples are the same, while the genes are combinated, i.e. \(i \neq k, m \neq o, j = n, l = p, \). combs = "all": reference genes, genes-of-interest, control and treatment samples are all combinated, i.e. \(i \neq k, m \neq o, j \neq n, l \neq p\).

The last setting rarely makes sense and is very time-intensive. combs = "same" is the most common setting, but combs = "across" also makes sense if different genes-of-interest and reference gene combinations should be calculated for the same samples.

ratioPar has an option of averaging several reference genes, as described in Vandesompele et al. (2002). Threshold cycles and efficiency values for any \(i\) reference genes with \(j\) replicates are averaged before calculating the ratios using the averaged value \(\mu_r\) for all reference genes in a control/treatment sample. The overall error \(\sigma_r\) is obtained by error propagation. The whole procedure is accomplished by function refmean, which can be used as a stand-alone function, but is most conveniently used inside ratioPar setting refmean = TRUE. For details about reference gene averaging by refmean, see there.

References

Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. Vandesompele J, De Preter K, Pattyn F, Poppe B, Van Roy N, De Paepe A, Speleman F. Genome Biol (2002), 3: research0034-research0034.11.

Examples

# NOT RUN {
## One control sample, two treatment samples, 
## one gene-of-interest, two reference genes, 
## two replicates each. Replicates are averaged,
## but reference genes not, so that we have 4 ratios.
GROUP1 <- c("r1c1", "r1c1", "r2c1", "r2c1", "g1c1", "g1c1",
           "r1s1", "r1s1", "r1s2", "r1s2", "r2s1", "r2s1",
           "r2s2", "r2s2", "g1s1", "g1s1", "g1s2", "g1s2") 
EFF1 <- c(1.96, 2.03, 1.60, 1.67, 1.91, 1.97, 1.53, 1.61, 1.87, 
          1.92, 1.52, 1.58, 1.84, 1.90, 1.49, 1.56, 1.83, 1.87)
CP1 <- c(15.44, 15.33, 14.84, 15.34, 18.89, 18.71, 18.13, 17.22, 22.06, 
        21.85, 21.03, 20.92, 25.34, 25.12, 25.00, 24.62, 28.39, 28.28)
RES1 <- ratioPar(group = GROUP1, effVec = EFF1, cpVec= CP1, refmean = FALSE)


# }
# NOT RUN {
## Same as above, but now we average the two
## reference genes, so that we have 2 ratios.
RES2 <- ratioPar(group = GROUP1, effVec = EFF1, cpVec= CP1, refmean = TRUE)

## Two control samples, one treatment sample, 
## one gene-of-interest, one reference gene, 
## no replicates. Reference gene has efficiency = 1.8,
## gene-of-interest has efficiency = 1.9.
GROUP3 <- c("r1c1", "r1c2", "g1c1", "g1c2", 
            "r1s1", "g1s1") 
EFF3 <- c(1.8, 1.8, 1.9, 1.9, 1.8, 1.9)
CP3 <- c(17.25, 17.38, 22.52, 23.18, 21.42, 19.83)
RES3 <- ratioPar(group = GROUP3, effVec = EFF3, cpVec= CP3, refmean = TRUE)
                   
## One control sample, one treatment sample, 
## three genes-of-interest, no reference gene, 
## three replicates. Using efficiency from sigmoidal model. 
GROUP4 <- c("g1c1", "g1c1", "g1c1", "g2c1", "g2c1", "g2c1", "g3c1", "g3c1", "g3c1",
            "g1s1", "g1s1", "g1s1", "g2s1", "g2s1", "g2s1", "g3s1", "g3s1", "g3s1")
EFF4 <- c(1.79, 1.71, 1.83, 1.98, 1.85, 1.76, 1.76, 1.91, 1.84, 1.80, 1.79, 1.91,
          1.88, 1.79, 1.78, 1.89, 1.86, 1.81)
CP4 <- c(15.68, 15.84, 14.47, 14.96, 18.97, 19.04, 17.65, 16.76, 22.11, 22.03, 20.43, 
         20.36, 25.29, 25.29, 24.27, 23.99, 28.34, 28.38)
RES4 <- ratioPar(group = GROUP4, effVec = EFF4, cpVec= CP4, refmean = TRUE)

## Compare to REST software using the data from the 
## REST 2008 manual (http://rest.gene-quantification.info/)
cp.rc <- c(26.74, 26.85, 26.83, 26.68, 27.39, 27.03, 26.78, 27.32)
cp.rs <- c(26.77, 26.47, 27.03, 26.92, 26.97, 26.97, 26.07, 26.3, 26.14, 26.81)
cp.gc <- c(27.57, 27.61, 27.82, 27.12, 27.76, 27.74, 26.91, 27.49)
cp.gs <- c(24.54, 24.95, 24.57, 24.63, 24.66, 24.89, 24.71, 24.9, 24.26, 24.44)
eff.rc <- rep(1.97, 8)
eff.rs <- rep(1.97, 10)
eff.gc <- rep(2.01, 8)
eff.gs <- rep(2.01, 10)
CP5 <- c(cp.rc, cp.rs, cp.gc, cp.gs)
EFF5 <- c(eff.rc, eff.rs, eff.gc, eff.gs) 
GROUP5 <- rep(c("r1c1", "r1s1", "g1c1", "g1s1"), c(8, 10, 8, 10))
RES5 <- ratioPar(group = GROUP5, effVec = EFF5, cpVec = CP5)
RES5$resDat
# }